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Instance Image Retrieval with Generative Adversarial Training

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MultiMedia Modeling (MMM 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11961))

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Abstract

While generative adversarial training becomes promising technology for many computer vision tasks especially in image processing domain, it has few works so far on instance level image retrieval domain. In this paper, we propose an instance level image retrieval method with generative adversarial training (ILRGAN). In this proposal, adversarial training is adopted in the retrieval procedure. Both generator and discriminator are redesigned for retrieval task: the generator tries to retrieve similar images and passes them to the discriminator. And the discriminator tries to discriminate the dissimilar images from the images retrieved and then passes the decision to the generator. Generator and discriminator play min-max game until the generator retrieves images that the discriminator can not discriminate the dissimilar images. Experiments on four widely used databases show that adversarial training really works for instance level image retrieval and the proposed ILRGAN can get promising retrieval performances.

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Acknowledgement

This work is funded by Zhejiang Provincial Natural Science Foundation of China under Grant No. LY18F020032, Natural Science Foundation of China under Grant No. 61976192, 61502424 and U1509207. The source code of this work will be released on http://www.escience.cn/people/congbai/index.html.

Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China

    Hongkai Li, Cong Bai & Ling Huang

  2. College of Computer Science, Fudan University, Shanghai, China

    Yugang Jiang

  3. College of Computer Science and Engineering, Tianjin University of Technology, Tianjin, China

    Shengyong Chen

Authors
  1. Hongkai Li
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  2. Cong Bai
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  3. Ling Huang
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  4. Yugang Jiang
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  5. Shengyong Chen
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Corresponding author

Correspondence to Cong Bai .

Editor information

Editors and Affiliations

  1. Korea Advanced Institute of Science and, Daejeon, Korea (Republic of)

    Yong Man Ro

  2. National Chiao Tung University, Hsinchu, Taiwan

    Wen-Huang Cheng

  3. Korea Advanced Institute of Science and Technology, Daejeon, Korea (Republic of)

    Junmo Kim

  4. National Cheng Kung University, Tainan City, Taiwan

    Wei-Ta Chu

  5. Tsinghua University, Beijing, China

    Peng Cui

  6. Korea Advanced Institute of Science and Technology, Daejeon, Korea (Republic of)

    Jung-Woo Choi

  7. National Tsing Hua University, Hsinchu, Taiwan

    Min-Chun Hu

  8. Ghent University, Ghent, Belgium

    Wesley De Neve

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Li, H., Bai, C., Huang, L., Jiang, Y., Chen, S. (2020). Instance Image Retrieval with Generative Adversarial Training. In: Ro, Y., et al. MultiMedia Modeling. MMM 2020. Lecture Notes in Computer Science(), vol 11961. Springer, Cham. https://doi.org/10.1007/978-3-030-37731-1_31

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  • DOI: https://doi.org/10.1007/978-3-030-37731-1_31

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-37731-1

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